1. Field of Invention
The current invention relates to thermal imaging, and more particularly to three-dimensional thermal imaging for the detection of skin lesions and other natural and abnormal conditions.
2. Discussion of Related Art
Thermoregulation is the mechanism that maintains the temperature of the human body within desired boundaries and responds to changes in the ambient and other external and internal variations (caused by disease, physical activity, mechanical or chemical stress and other factors) by controlling the rates of heat generation and heat loss. Thermoregulation is one aspect of homeostasis, a dynamic state of stability between the internal and external environments. The temperature distribution in the human body depends not only on physical parameters, but also on the physiology associated with the homeostatic and metabolic processes as well as the structure and dynamics of the tissue, vascular and nervous systems. There is a large body of evidence that disease or deviation from normal functioning are accompanied by changes in temperature of the body, which again affect the temperature of the skin (Jones, 1998, Anbar, 1998). The behavior of tumors differs from that of healthy tissue in terms of heat generation, because of processes such as inflammation, increase in metabolic rate, interstitial hypertension, abnormal vessel morphology and lack of response to homeostatic signals (Ring, 1998, Anbar, 2002). Clearly, accurate data about the temperature of the human body and skin can provide a wealth of information on the processes responsible for heat generation and thermoregulation, in particular the deviation from normal conditions, often caused by disease.
Infrared thermography is a non-invasive tool that allows the measurement of the spatial and temporal variations of temperature associated with the IR radiation emitted by the object under study, which is the human body in embodiments of the current invention. The emitted radiation and the skin surface temperature carry a wealth of information about different processes within the human body. The advances in IR cameras, computers and imaging techniques led to increasing interest in infrared thermography in medical diagnostics and other biomedical and engineering applications (Gulyaev et al. 1995, Jones and Plassmann, 2002, Vaviov et al. 2001). Past applications of infrared thermography focused on the imaging of relatively small areas of the body, and the imaging was often qualitative, with a low resolution.
Cancer is the second leading cause of death in the United States as well as worldwide. New techniques to detect cancer at an early stage are being explored in numerous laboratories and research centers all over the world, and noninvasive diagnostic tools are of particular interest. Each technique offers unique advantages and disadvantages, many of which pose a compromise between effectiveness and accuracy versus cost considerations and invasiveness. While the precision of MRI is unmatched in detecting internal lesions, infrared imaging has been proven useful in the study of skin and subsurface tumors such as breast cancer, skin cancers (melanoma), as well as arteriosclerosis, peripheral vascular disease, burn injuries, infections, Lyme disease lesions, pressure ulcers, contamination with chemicals (in chemical warfare and homeland security), athletic performance, etc (Ng, 2008, Carlo, 1995, Merla et al. 2007, Helmy et al. 2008, Tan et al. 2009, Vainer, 2005, Oliveira et al. 2007, Wang et al. 2004, Merla et al. 2010).
3d Topographic Mapping of the Human Body.
Accurate 3d imaging and reconstruction of the human body in general, or that of a specific human subject, is of interest in numerous engineering (military and aerospace—virtual reality simulations; entertainment—computer games, movies, animation; industrial—development of equipment, protective clothing, etc.) and medical applications (diagnostics, rehabilitation, prosthetics, analysis and enhancement of athletic performance). Spatial accuracy is of primary concern in medical diagnostics applications when 3D data is captured for a real human body.
There are two basic categories of technologies employed in human body imaging/scanning: laser-based technologies and Moiré fringing. Both technologies are optical and they do not require direct contact. In the laser-based technology, a laser beam is projected from eight laser diodes onto the body, scanning it from top to bottom. The laser stripe deformed by the body surface is captured and recorded by different cameras strategically positioned around the body. The recorded data represent the location of the laser stripe with respect to the camera reference. A software program combines the data from each camera using triangulation and produces a set of 3D data points representing the body surface. The duration of the scan is around 17 s. The number of 3d data points generated is of the order of 100,000 points (Werghi, 2007).
Moiré imaging techniques have been used to measure surface topography in a variety of medical and engineering applications. Typically the surface area analyzed is smaller than what is required in full-body imaging. There is a significant body of literature that analyzes the factors that affect the formation of the fringes, such as gratings, light sources of finite dimensions, intensity distribution of the light source, viewing aperture function, lateral displacement of the source relative to the viewing axis, the viewing distance, grating spacing and grating ratio, etc.
Moiré fringing technology employs a Moiré-based light projection system, also known as phase measuring profilometry. In this system, a white light source is used to project contour patterns (sinusoidal fringes) on the body surface and these patterns, distorted by curves in the body surface, are detected by a set of cameras arranged around the subject. The superimposed deformed patterns generated in this way interact with other patterns used as reference points and form fringes that describe the body surface contours. Subsequently, the data obtained from the separate fringes are combined into a single reference, yielding a cloud in which the 3D data points represent the body surface. Scan time in this technology is around 6 s. The average data density of scanner is 800,000 points (Werghi, 2007).
Human body scanning can provide clouds of more than 200,000 points representing the 3D human body. However, getting an accurate model from scanned data is not a straightforward task. The three main areas of research in managing scanned data are human body modeling, body landmark detection, and segmentation (Werghi, 2007). Segmentation provides a labeling of the body that can be used to reduce the search space for body landmarks. In dynamic human body modeling, segmentation has contributed to solving problems arising in data registration. By identifying body parts in different poses, it permits important correspondences between the related data sets. There have been many research efforts put into these post-processing of scanned data. Wang (2005) developed an algorithm to extract key features on the human body, and then built a set of parametric surfaces to represent the scanned subject. Luginbuhlt et al. 2009 used a generic model which is segmented and points are organized in slices. They adapted the sizes of each body limb and then fitted each slice to the data. Leong et al. 2007 employed image processing and computational geometry techniques to identify, automatically, body features from a markerless scanned body.
Gu et al. (1998) developed hardware and software to model the human body using laser-based technology. The hardware consists of the equipment-supporting framework, cameras and lenses, lighting arrangement and sensors for synchronization. 12-13 cameras are used in the system. Six views are used to cover the 360° circular reconstruction space. An additional view is needed for the frontal image to obtain better information of the chest, the armpit and the crotch. The target object to be imaged is up to 2 m tall, so two cameras are used for each viewpoint, in order for the full length of the object to be imaged. The imaging software consists of five modules: camera calibration, image capturing, image processing, model reconstruction and virtual measurement modules. During the imaging process, the human subject is required to wear tight clothing and stand in the center of the imaging space so that the body can be visible to all of the cameras. To obtain clear contours of the body, the subject is asked to open his or her arms and also to keep his or her two legs slightly apart.
Another study of particular interest is the one by Godil (2007). This author relied on the CAESAR anthropometric database (a database of approximately 5000 3d scans of human bodies). They developed two representations based on human body shape: (i) a descriptor vector based on lengths, mostly between single large bones (3D body description vector of fifteen distances, such as wrist to elbow, elbow to shoulder, hip to knee, etc.) as well as (ii) three silhouettes of the human body are created by rendering the human body from the front, side and top. In addition to the human body, they developed two representations for the human head.
IR Imaging: Medical Applications and Potential for Full Body Scanning.
Measuring and visualizing the local skin temperature is a useful approach to diagnose the signs of disease. IR imaging is a non-invasive method that measures spatial variations in skin temperature that can be caused by a variety of conditions, e.g., contusions, fractures, burns, carcinomas, lymphomas, dermatological diseases, rheumatoid arthritis, diabetes mellitus, bacterial infections, etc. These conditions are commonly associated with regional vasodilation, hyperthermia, hyperperfusion, hypermetabolism, and hypervascularization, that manifest as a heat source associated with higher temperature.
IR imaging is especially suitable for diagnosing peripheral vascular disorders, inflammatory disease, tumors, local metabolic disorders, and body temperature abnormalities. So far IR systems have been used to diagnose breast cancer (Ng, 2008), rheumatism (Ring, 1998), skin lesions (Di Carlo, 1995), fever (Ng and Acharya 2008), impotence (Merla et al. 2007), thyroid gland disease (Helmy et al. 2008) and eye diseases (Tan et al. 2009). IR imaging also assists in decision making in open-heart surgery due to its ability to provide real-time information (Ruddock, 2008). It was applied for the management of neuropathic pain (Hooshmand et al. 2001), the assessment of patient response to chiropractic care by measuring the temperature gradients in clinical setting (Stiliwagon, 1998), the evaluation of cerebral thermoregulation (facial thermography) (Oliveira et al. 2007) and sweat glands (Vainer, 2005). In addition to considering abnormalities, IR imaging is also used to visualize the human body anterior cutaneous temperature variations in well-trained runners and to quantitatively evaluate the specific cutaneous whole body thermal adaptations that occur during and after graded physical activity (Merla et al. 2010).
In IR imaging, diagnosis is often made by comparing temperature asymmetry between healthy and diseased regions of the body: a change in temperature is considered to be a sign of abnormal functioning (Vaviov et al. 2001). Anbar (1998) found that the diagnosis of neurological, musculoskeletal or other tissue abnormalities by IR imaging is based on pathological changes in the spatial distribution of temperature over the skin surface as well as pathological changes in the dynamic behavior, i.e., warming, cooling, or periodic modulation of temperature of a given subarea of the skin.
In order to use IR imaging for clinical diagnosis, it is necessary to determine the location of the abnormal thermal areas as well as the degree of change in body-surface temperature. It is useful to cross-reference the resulting IR images with visual images of the patient and then the segment region of interest in IR images to locate the subregions of interest (Schaefer et al. 2006, Yoon et al. 2006). Mabuchi et al. 1998 divided the body surface into two symmetrical sections: the healthy side and the affected side. Each part was then further divided into the same number of symmetrical trapezoidal or triangular pairs. Ring et al. 2004 specified a total of 27 views of the body and defined 87 regions of interest (ROI) in terms of the shape of the area for each view. The mean temperature and standard deviations of the temperatures within the ROIs and along the cross-sections are compared in the diagnostic imaging process.
IR Imaging in the Diagnosis of Breast Cancer.
Despite advances in treatment that have reduced mortality, breast cancer remains the second leading cause of cancer deaths in women today (Kennedy et al. 2009). Clinical breast exam and mammography are the two most widely used tools to screen for breast cancer. In addition to these methods, IR imagining has been approved by the FDA since 1982 as a screening tool for breast cancer. IR imaging was first introduced as a screening tool in 1956, after the observation of asymmetric hot spots and vascularity in infrared images of breast cancer patients. When a malignant tumor develops in a breast, it will cause prominent localized increase of skin surface temperature due to the high metabolic activity and blood perfusion of the tumor (Jones 1998). This localized temperature difference shows up as a spot or vascular pattern in a breast infrared image that is called a heat pattern. Breast cancer can be detected through the visual analysis of thermal patterns by physicians (Jones, 1998, Keyserlingk et al. 2000, Head et al. 2000, Ng et al. 2001).
Feig et al. (1977) compared the sensitivity of steady-state (active) IR imaging to other methods of breast cancer detection. The result of this study showed that active IR imaging has a sensitivity of only 39% and a specificity of 82%. The major difficulty in the interpretation of breast IR imaging is the complexity of the vascular patterns (false negatives and false positives) and it is reported that a high thermal gradient for a hot spot over a tumor was the most important factor to differentiate a malignancy from a benign condition (Jones 1998, Keyserlingk et al. 2000, Head et al. 2000, Ng et al. 2001). Therefore, in order to better differentiate breast cancer from benign breast disease, IR imaging should be done during thermal recovery (dynamic mapping). Since then, many attempts have been made to diagnose breast cancer with dynamic IR imaging and several methods of evaluation have been proposed in order to improve its diagnostic value (Jones, 1998, Keyserlingk et al. 2000, Head et al. 2000, Ng et al. 2001, Amalu, 2004, Tang et al. 2008, Arora et al. 2008, Ohashi and Uchida 2000). When a breast is exposed to cold stress, the vascular pattern disappears, and after the stress is removed, the pattern gradually recovers. This phenomenon of thermal recovery can be visualized by sequential IR imaging or by digital subtraction IR imaging (Ohashi et al. 1994). Keyserlingk et al. (1998) found the sensitivity of IR imaging to be 83%. The combination of mammography and IR imaging increased the sensitivity to 95%. In light of developments in computer technology and the maturing of the thermographic industry, additional improvements are required to develop this technology to provide effective noninvasive early detection of breast cancer (Kennedy et al. 2009).
Melanoma.
Melanoma incidence is increasing at one of the fastest rates for all cancers in the United States with a current lifetime risk of 1 in 58. Over 60,000 patients are expected to be diagnosed with melanoma in the US with more than 8,000 deaths in 2008. The reported 1-year survival rates for patients with advanced melanoma range from 40% to 60%, and systemic agents are not currently available to significantly extend the lifespan of patients with advanced disease. These statistics stress the need to detect melanomas at their earliest stages for chances of optimal cure and to identify patients with high-risk primary disease for the initiation of early prophylactic treatment.
The increased availability of thermal imaging cameras has led to a growing interest in the application of infrared imaging techniques to the detection and identification of subsurface structures both in engineering and in living systems. Infrared (IR) imaging is a non-contact sensing method concerned with the measurement of electromagnetic radiation in the infrared region of the spectrum (750 nm-100 μm). Radiation emitted by a surface at a given temperature is called spectral radiance and is defined by the Planck's distribution for the idealized case of a blackbody. Infrared cameras detect this radiation and the surface temperature distribution can be recovered after post-processing the sensor information and appropriate calibration. Since the surface temperature distribution depends on the properties of subsurface structures and regions, infrared imaging can be used to detect and identify subsurface structures by analyzing the differences in the thermal response of an undisturbed region such as healthy skin and a near-surface structure of different properties such as a skin lesion.
Infrared imaging can be performed either passively or actively (dynamically). Passive infrared imaging involves, in its simples form, the visualization of the emitted radiation in the infrared region of the electromagnetic spectrum, for example night vision goggles, and, in more advanced imaging applications, measuring (after post processing of the information acquired by the sensor and appropriate calibration) temperature variations of structures whose temperature naturally differs from ambient temperature or varies locally due to internal heat sources. Active infrared imaging involves introducing external forcing such as heating or cooling to induce and/or enhance relevant thermal contrasts observed on the surface. The latter technique is based on the following principle: when a surface is heated or cooled, variations in the thermal properties of a structure located underneath the surface result in identifiable temperature contours on the surface itself, differing from those present in the steady-state situation during passive imaging as well as from the surrounding regions. These contours are characteristic of the thermal properties of the base structure and subsurface perturbations, and can, when combined with a suitable model, provide information regarding the shape and depth of the perturbation (a lesion in our study). Thus, the dynamic thermal response of the structure obtained using the active imaging provides additional information useful in the identification of the perturbation when compared to information obtained by passive imaging.
Infrared imaging has been successfully applied in various problems in engineering and medicine. Recent improvements in infrared sensor and computer technology led to the resurgence of infrared imaging in medicine. In particular, describing the thermal response of chemically and metabolically active multilayered samples constitutes an important problem. For instance, thermal modeling of temperature distributions linked to large blood vessels has received a great deal of attention in the research community (Hundhausen, E and Theves B 1979 Eur. J. Appl. Physiol. Occup. Physiol. 40(4) 235-44; Lemons D E, Chien S, Crawshaw L I, Weinbaum S and Jiji L M 1987, Am. J. Physiol. 253 128-35; Nitzan M, Mahler Y, Roberts J, Khan O, Gluck E, Roberts V C and Baum M 1989, Clin. Phys. Physiol. Meas. 10 337-41; Zhu L and Weinbaum S 1995, J. Biomech. Eng. 117 64-73; Nakagawa A, T. Hirano, Uenohara H, Sato M, Kusaka Y, Shirane R, Takayama K and Yoshimoto T 2003, Minim. Invasive Neurosurg. 46(4) 231). Shrivastava et al (Shrivastava D, McKay B and Roemer R B 2005, J. Heat Trans. 127 179-88) derived an analytical model describing the tissue temperature distribution in unheated/heated, finite, noninsulated tissue with a pair of vessels to quantify the vessel-vessel and vessel-tissue heat transfer rate. He et al (He Y, Liu H, Himeno R, Sunaga J, Kakusho H and Yokota H 2008, Comp. Bio. Med. 38 555-62) developed a FEM model based on the heat transport in porous media to simulate blood flow in large vessels and living tissue. Boue et al (Boue C, Cassagne F, Massoud C and Fournier D 2007, Infrared Phys. Tech. 51 13-20) analyzed the infrared images to extract the radius, depth and the blood flow velocity in a vein.
In order to understand the physics of IR imaging in the analysis of lesions, first, it is worth recalling that the chemical reactions, blood transport, perfusion and metabolic processes that affect local temperature response in normal tissue are under both global and local control (Gulyaev Y V Markov A G Koreneva L G and Zakharov P V 1995, IEEE Eng. Med. Bio. 14(6) 766-71; Jones B F and Plassmann P 2002, IEEE Eng. Med. Bio. 21(6) 41-48; Otsuka K, Okada S, Hassan M and Togawa T 2002, IEEE Eng. Med. Bio. 21(6) 49-55; Kakuta N, Yokoyama S and Mabuchi K 2002, IEEE Eng. Med. Bio. 21(6) 65-72). When a cancerous lesion develops, affected tissue has escaped from the control of the various feedback systems and mechanisms present in healthy tissue, leading to such abnormal processes as cell proliferation, disordered spatial organization and excess metabolism i.e. heat generation (Brown S L, Hunt J W and Hill R P 1992, Int. J. Hyperthermia 8(4) 501-14; Jones B F 1998, IEEE Trans. Med. Imaging 17(6) 1019-27). Examples of such a response include Kaposi Sarcoma, melanoma, neuroblastoma, wine stain birthmarks, breast cancer, etc. (Ahuja A S, Prasad K N, Hendee W R and Carson P L 1978, Med. Phys. 5(5) 418-21; Anvari B, Tanenbaum B S, Milner T E, Kimel S, Svaasand L O and Nelson J S 1995, Phys. Med. Biol. 40(9) 1451-65, Head J F and Elliott R L 2002, IEEE Eng. Med. Bio. 21(6) 80-85; Xianwu T, Haishu D, Guangzhi W and Zhongqi L 2004, Proc. 26th IEEE EMBS Ann. Int. Conf. 873-8; Deng Z and Liu J 2005, Proc. 27th IEEE EMBS Ann. Int. Conf. 7525-8; Buzug T M, Schumann S, Pfaffmann L, Reinhold U and Ruhlmann J 2006, Proc. 28th IEEE EMBS Ann. Int. Conf. 2766; Mital M and Scott E P 2007, J. Biomech. Eng. 129 33-9).
Although there has been significant interest in IR based systems for medical applications, they have remained of limited usefulness. Therefore, there a need in the art for improved high-resolution thermal imaging systems.